# Source code for abydos.distance._fager_mcgowan

# Copyright 2018-2020 by Christopher C. Little.
# This file is part of Abydos.
#
# Abydos is free software: you can redistribute it and/or modify
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Abydos is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
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# You should have received a copy of the GNU General Public License
# along with Abydos. If not, see <http://www.gnu.org/licenses/>.

"""abydos.distance._fager_mcgowan.

Fager & McGowan similarity
"""

from ._token_distance import _TokenDistance

__all__ = ['FagerMcGowan']

[docs]class FagerMcGowan(_TokenDistance):
r"""Fager & McGowan similarity.

For two sets X and Y, the Fager & McGowan similarity
:cite:Fager:1957,Fager:1963 is

.. math::

sim_{FagerMcGowan}(X, Y) =
\frac{|X \cap Y|}{\sqrt{|X|\cdot|Y|}} -
\frac{1}{2\sqrt{max(|X|, |Y|)}}

In :ref:2x2 confusion table terms <confusion_table>, where a+b+c+d=n,
this is

.. math::

sim_{FagerMcGowan} =
\frac{a}{\sqrt{(a+b)(a+c)}} - \frac{1}{2\sqrt{max(a+b, a+c)}}

"""

def __init__(self, tokenizer=None, intersection_type='crisp', **kwargs):
"""Initialize FagerMcGowan instance.

Parameters
----------
tokenizer : _Tokenizer
A tokenizer instance from the :py:mod:abydos.tokenizer package
intersection_type : str
Specifies the intersection type, and set type as a result:
See :ref:intersection_type <intersection_type> description in
:py:class:_TokenDistance for details.
**kwargs
Arbitrary keyword arguments

Other Parameters
----------------
qval : int
The length of each q-gram. Using this parameter and tokenizer=None
will cause the instance to use the QGram tokenizer with this
q value.
metric : _Distance
A string distance measure class for use in the soft and
fuzzy variants.
threshold : float
A threshold value, similarities above which are counted as
members of the intersection for the fuzzy variant.

"""
super(FagerMcGowan, self).__init__(
tokenizer=tokenizer, intersection_type=intersection_type, **kwargs
)

[docs]    def sim_score(self, src, tar):
"""Return the Fager & McGowan similarity of two strings.

Parameters
----------
src : str
Source string (or QGrams/Counter objects) for comparison
tar : str
Target string (or QGrams/Counter objects) for comparison

Returns
-------
float
Fager & McGowan similarity

Examples
--------
>>> cmp = FagerMcGowan()
>>> cmp.sim_score('cat', 'hat')
0.25
>>> cmp.sim_score('Niall', 'Neil')
0.16102422643817918
>>> cmp.sim_score('aluminum', 'Catalan')
-0.048815536468908724
>>> cmp.sim_score('ATCG', 'TAGC')
-0.22360679774997896

"""
if not src or not tar:
return 0.0

self._tokenize(src, tar)

a = self._intersection_card()
apb = self._src_card()
apc = self._tar_card()

first = a / (apb * apc) ** 0.5 if a else 0.0
second = 1 / (2 * (max(apb, apc) ** 0.5))

return first - second

[docs]    def sim(self, src, tar):
r"""Return the normalized Fager & McGowan similarity of two strings.

As this similarity ranges from :math:(-\inf, 1.0), this normalization
simply clamps the value to the range (0.0, 1.0).

Parameters
----------
src : str
Source string (or QGrams/Counter objects) for comparison
tar : str
Target string (or QGrams/Counter objects) for comparison

Returns
-------
float
Normalized Fager & McGowan similarity

Examples
--------
>>> cmp = FagerMcGowan()
>>> cmp.sim('cat', 'hat')
0.25
>>> cmp.sim('Niall', 'Neil')
0.16102422643817918
>>> cmp.sim('aluminum', 'Catalan')
0.0
>>> cmp.sim('ATCG', 'TAGC')
0.0